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C8000 | color difference | |
C8001 | The Wald Chi-Square test statistic is the squared ratio of the Estimate to the Standard Error of the respective predictor. The probability that a particular Wald Chi-Square test statistic is as extreme as, or more so, than what has been observed under the null hypothesis is given by Pr > ChiSq. | |
C8002 | The chi-squared test applies an approximation assuming the sample is large, while the Fisher's exact test runs an exact procedure especially for small-sized samples. | |
C8003 | Feature transformation is simply a function that transforms features from one representation to another. feature values may cause problems during the learning process, e.g. data represented in different scales. | |
C8004 | Gradient Boosting or GBM is another ensemble machine learning algorithm that works for both regression and classification problems. GBM uses the boosting technique, combining a number of weak learners to form a strong learner. We will use a simple example to understand the GBM algorithm. | |
C8005 | He doesn't explicitly betray Kaneki, but it seems like it because someone who seemed like such a nice guy, giving advice to Kaneki and helping retrieve him from Aogiri, ended up being a sadistic and manipulative person. | |
C8006 | An example of statistics is a report of numbers saying how many followers of each religion there are in a particular country. An example of statistics is a math class offered in high schools and colleges. The definition of a statistic is a number, or a person who is an unnamed piece of data to be studied. | |
C8007 | The input layer (often called a feature vector) has a node for each feature used for prediction and usually an extra bias node. You usually need only 1 hidden layer, and discerning its ideal size tricky. Having too many hidden layer nodes can result in overfitting and slow training. | |
C8008 | T-test. A t-test is used to compare the mean of two given samples. Like a z-test, a t-test also assumes a normal distribution of the sample. A t-test is used when the population parameters (mean and standard deviation) are not known. | |
C8009 | The moment generating function M(t) can be found by evaluating E(etX). By making the substitution y=(λ−t)x, we can transform this integral into one that can be recognized. And therefore, the standard deviation of a gamma distribution is given by σX=√kλ. | |
C8010 | The Bernoulli distribution is a discrete probability distribution that covers a case where an event will have a binary outcome as either a 0 or 1. | |
C8011 | The one-way multivariate analysis of variance (one-way MANOVA) is used to determine whether there are any differences between independent groups on more than one continuous dependent variable. In this regard, it differs from a one-way ANOVA, which only measures one dependent variable. | |
C8012 | Some applications of unsupervised machine learning techniques include: Clustering allows you to automatically split the dataset into groups according to similarity. Often, however, cluster analysis overestimates the similarity between groups and doesn't treat data points as individuals. | |
C8013 | Finding and Making the RulesFrequent Itemset Generation:- find all itemsets whose support is greater than or equal to the minimum support threshold.Rule generation: generate strong association rules from the frequent itemset whose confidence greater than or equal to minimum confidence threshold. | |
C8014 | Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. | |
C8015 | Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Intuitively, underfitting occurs when the model or the algorithm does not fit the data well enough. Specifically, underfitting occurs if the model or algorithm shows low variance but high bias. | |
C8016 | The cumulative distribution function (c.d.f.) of a discrete random variable X is the function F(t) which tells you the probability that X is less than or equal to t. So if X has p.d.f. P(X = x), we have: F(t) = P(X £ t) = SP(X = x). | |
C8017 | The Z score is a test of statistical significance that helps you decide whether or not to reject the null hypothesis. The p-value is the probability that you have falsely rejected the null hypothesis. Z scores are measures of standard deviation. Both statistics are associated with the standard normal distribution. | |
C8018 | Cohen's kappa coefficient (κ) is a statistic that is used to measure inter-rater reliability (and also Intra-rater reliability) for qualitative (categorical) items. | |
C8019 | In machine learning, early stopping is a form of regularization used to avoid overfitting when training a learner with an iterative method, such as gradient descent. Such methods update the learner so as to make it better fit the training data with each iteration. | |
C8020 | Coverage, is the extent to which the real, observed population matches the ideal or normative population. A population is the domain from which observations for a particular topic can be drawn. | |
C8021 | Basically, the test compares the fit of two models. The null hypothesis is that the smaller model is the “best” model; It is rejected when the test statistic is large. In other words, if the null hypothesis is rejected, then the larger model is a significant improvement over the smaller one. | |
C8022 | In data parallel model, tasks are assigned to processes and each task performs similar types of operations on different data. Data parallelism is a consequence of single operations that is being applied on multiple data items. Data-parallel model can be applied on shared-address spaces and message-passing paradigms. | |
C8023 | The major difference between machine learning and statistics is their purpose. Machine learning models are designed to make the most accurate predictions possible. Statistical models are designed for inference about the relationships between variables. | |
C8024 | At the pooling layer, forward propagation results in an N×N pooling block being reduced to a single value - value of the “winning unit”. Backpropagation of the pooling layer then computes the error which is acquired by this single value “winning unit”. | |
C8025 | A priori probability refers to the likelihood of an event occurring when there is a finite amount of outcomes and each is equally likely to occur. The outcomes in a priori probability are not influenced by the prior outcome. A priori probability is also referred to as classical probability. | |
C8026 | Average (or mean) filtering is a method of 'smoothing' images by reducing the amount of intensity variation between neighbouring pixels. The average filter works by moving through the image pixel by pixel, replacing each value with the average value of neighbouring pixels, including itself. | |
C8027 | The standard error tells you how accurate the mean of any given sample from that population is likely to be compared to the true population mean. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean. | |
C8028 | Box-Cox Transformation is a type of power transformation to convert non-normal data to normal data by raising the distribution to a power of lambda (λ). The algorithm can automatically decide the lambda (λ) parameter that best transforms the distribution into normal distribution. | |
C8029 | Basically, you're just pre-setting some of the weights of the new network. Be sure to initialize the new connections to have similar distributions. Make the last layer a concatenation of their results and then add another few layers. Make the last layer a concatenation of their results and the original input. | |
C8030 | Article. Cards. TF-IDF is an abbreviation for Term Frequency-Inverse Document Frequency and is a very common algorithm to transform text into a meaningful representation of numbers. The technique is widely used to extract features across various NLP applications. | |
C8031 | It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. | |
C8032 | SVM tries to finds the “best” margin (distance between the line and the support vectors) that separates the classes and this reduces the risk of error on the data, while logistic regression does not, instead it can have different decision boundaries with different weights that are near the optimal point. | |
C8033 | In regression analysis, the dependent variable is denoted "Y" and the independent variables are denoted by "X". | |
C8034 | Statistical power, or the power of a hypothesis test is the probability that the test correctly rejects the null hypothesis. That is, the probability of a true positive result. statistical power is the probability that a test will correctly reject a false null hypothesis. | |
C8035 | A CNN LSTM can be defined by adding CNN layers on the front end followed by LSTM layers with a Dense layer on the output. It is helpful to think of this architecture as defining two sub-models: the CNN Model for feature extraction and the LSTM Model for interpreting the features across time steps. | |
C8036 | If our model is too simple and has very few parameters then it may have high bias and low variance. This tradeoff in complexity is why there is a tradeoff between bias and variance. An algorithm can't be more complex and less complex at the same time. | |
C8037 | Partial correlation holds variable X3 constant for both the other two variables. Whereas, Semipartial correlation holds variable X3 for only one variable (either X1 or X2). Hence, it is called 'semi'partial. There should be linear relationship between all the three variables. | |
C8038 | In the terminology of machine learning, classification is considered an instance of supervised learning, i.e., learning where a training set of correctly identified observations is available. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. | |
C8039 | Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. | |
C8040 | The measure of central tendency which is most strongly influenced by extreme values in the 'tail' of the distribution is: the mean. The mean height of a student group is 167 cm. | |
C8041 | A stochastic process is defined as a collection of random variables X={Xt:t∈T} defined on a common probability space, taking values in a common set S (the state space), and indexed by a set T, often either N or [0, ∞) and thought of as time (discrete or continuous respectively) (Oliver, 2009). | |
C8042 | The sample proportion is what you expect the results to be. This can often be determined by using the results from a previous survey, or by running a small pilot study. If you are unsure, use 50%, which is conservative and gives the largest sample size. | |
C8043 | Define spreading activation. The process through which activity in one node in a network flows outward to other nodes through associative links. | |
C8044 | This paper describes the concept of adaptive noise cancelling, an alternative method of estimating signals corrupted by additive noise or interference. The method uses a "primary" input containing the corrupted signal and a "reference" input containing noise correlated in some unknown way with the primary noise. | |
C8045 | The residual learning framework eases the training of these networks, and enables them to be substantially deeper — leading to improved performance in both visual and non-visual tasks. These residual networks are much deeper than their 'plain' counterparts, yet they require a similar number of parameters (weights). | |
C8046 | The Gini coefficient is equal to the area below the line of perfect equality (0.5 by definition) minus the area below the Lorenz curve, divided by the area below the line of perfect equality. | |
C8047 | An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”. | |
C8048 | The Spearman rank-order correlation coefficient (Spearman's correlation, for short) is a nonparametric measure of the strength and direction of association that exists between two variables measured on at least an ordinal scale. | |
C8049 | Nonstandard units of measurement are units of measurement that aren't typically used, such as a pencil, an arm, a toothpick, or a shoe. We can use just about anything as a nonstandard unit of measurement, as we saw was the case with Mr. FuzzyPaws. | |
C8050 | KNN works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression). | |
C8051 | clustering | |
C8052 | To create a stratified random sample, there are seven steps: (a) defining the population; (b) choosing the relevant stratification; (c) listing the population; (d) listing the population according to the chosen stratification; (e) choosing your sample size; (f) calculating a proportionate stratification; and (g) using | |
C8053 | Back-propagation is just a way of propagating the total loss back into the neural network to know how much of the loss every node is responsible for, and subsequently updating the weights in such a way that minimizes the loss by giving the nodes with higher error rates lower weights and vice versa. | |
C8054 | Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimizers help to get results faster. | |
C8055 | Control Charts: A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite). | |
C8056 | Here are 5 common machine learning problems and how you can overcome them.1) Understanding Which Processes Need Automation. 2) Lack of Quality Data. 3) Inadequate Infrastructure. 4) Implementation. 5) Lack of Skilled Resources. | |
C8057 | Training deep learning neural networks is very challenging. The best general algorithm known for solving this problem is stochastic gradient descent, where model weights are updated each iteration using the backpropagation of error algorithm. Optimization in general is an extremely difficult task. | |
C8058 | The regularization parameter (lambda) serves as a degree of importance that is given to miss-classifications. SVM pose a quadratic optimization problem that looks for maximizing the margin between both classes and minimizing the amount of miss-classifications. For non-linear-kernel SVM the idea is the similar. | |
C8059 | A random variate is a variable generated from uniformly distributed pseudorandom numbers. Depending on how they are generated, a random variate can be uniformly or nonuniformly distributed. Random variates are frequently used as the input to simulation models (Neelamkavil 1987, p. 119). | |
C8060 | So you can see that the ch-sq is the statistical measurement, while the P value is the level of probability that the result was due to chance alone. As the chi-sq statistic becomes larger, the P value becomes smaller. | |
C8061 | A decision tree is a specific type of flow chart used to visualize the decision making process by mapping out different courses of action, as well as their potential outcomes. | |
C8062 | λ(t)=f(t)S(t), which some authors give as a definition of the hazard function. In words, the rate of occurrence of the event at duration t equals the density of events at t, divided by the probability of surviving to that duration without experiencing the event. λ(t)=−ddtlogS(t). | |
C8063 | The objective of Unsupervised Anomaly Detection is to detect previously unseen rare objects or events without any prior knowledge about these. The only information available is that the percentage of anomalies in the dataset is small, usually less than 1%. | |
C8064 | The short answer is yes—because most regression models will not perfectly fit the data at hand. If you need a more complex model, applying a neural network to the problem can provide much more prediction power compared to a traditional regression. | |
C8065 | Correction factor is defined / given by. Square of the gross total of observed values /Total number of observed values. The sum of squares (SS), used in ANOVA, is actually the sum of squares of the deviations of observed values from their mean. | |
C8066 | In factorial ANOVA, each level and factor are paired up with each other (“crossed”). This helps you to see what interactions are going on between the levels and factors. If there is an interaction then the differences in one factor depend on the differences in another. | |
C8067 | When a data set has outliers or extreme values, we summarize a typical value using the median as opposed to the mean. When a data set has outliers, variability is often summarized by a statistic called the interquartile range, which is the difference between the first and third quartiles. | |
C8068 | In machine learning, the vanishing gradient problem is encountered when training artificial neural networks with gradient-based learning methods and backpropagation. The problem is that in some cases, the gradient will be vanishingly small, effectively preventing the weight from changing its value. | |
C8069 | Spatial pooling mimics the action of the receptive fields of the various layers of the cortex, primarily layers L2/3, L5 & L6. This also incorporates the inhibitory action of the inter-neurons. This inhibitory bit is simulated with the k-winner part of the Numenta implementation. | |
C8070 | Multivariate analysis is a set of statistical techniques used for analysis of data that contain more than one variable. Multivariate analysis refers to any statistical technique used to analyse more complex sets of data. | |
C8071 | How to Get Started with AIPick a topic you are interested in.Find a quick solution.Improve your simple solution.Share your solution.Repeat steps 1-4 for different problems.Complete a Kaggle competition.Use machine learning professionally. | |
C8072 | The easiest approach to dealing with categorical variables is to simply remove them from the dataset. This approach will only work well if the columns did not contain useful information. | |
C8073 | In the context of neural networks So, in a neural network context, the receptive field is defined as the size of the region in the input that produces the feature. Basically, it is a measure of association of an output feature (of any layer) to the input region (patch). | |
C8074 | Time series regression is a statistical method for predicting a future response based on the response history (known as autoregressive dynamics) and the transfer of dynamics from relevant predictors. Time series regression is commonly used for modeling and forecasting of economic, financial, and biological systems. | |
C8075 | It is a particular Monte Carlo method that numerically computes a definite integral. While other algorithms usually evaluate the integrand at a regular grid, Monte Carlo randomly chooses points at which the integrand is evaluated. This method is particularly useful for higher-dimensional integrals. | |
C8076 | Artificial Intelligence ExamplesManufacturing robots.Smart assistants.Proactive healthcare management.Disease mapping.Automated financial investing.Virtual travel booking agent.Social media monitoring.Inter-team chat tool.More items | |
C8077 | To import and publish data from TwitterClick the name of the dashboard to run it.From the toolbar, click the arrow next to the Add Data icon , and then select Import Data. The Connect to Your Data page opens. | |
C8078 | Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy. ndarray in Theano-compiled functions. | |
C8079 | k in kNN algorithm represents the number of nearest neighbor points which are voting for the new test data's class. If k=1, then test examples are given the same label as the closest example in the training set. | |
C8080 | The binomial distribution model allows us to compute the probability of observing a specified number of "successes" when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure. | |
C8081 | Let V be a vector space. A linearly independent spanning set for V is called a basis. Equivalently, a subset S ⊂ V is a basis for V if any vector v ∈ V is uniquely represented as a linear combination v = r1v1 + r2v2 + ··· + rkvk, where v1,,vk are distinct vectors from S and r1,,rk ∈ R. | |
C8082 | Partial correlation is a measure of the strength and direction of a linear relationship between two continuous variables whilst controlling for the effect of one or more other continuous variables (also known as 'covariates' or 'control' variables). | |
C8083 | Wright | |
C8084 | Other examples that may follow a Poisson distribution include the number of phone calls received by a call center per hour and the number of decay events per second from a radioactive source. | |
C8085 | Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training. Semi-supervised learning falls between unsupervised learning (with no labeled training data) and supervised learning (with only labeled training data). | |
C8086 | tl;dr: Bagging and random forests are “bagging” algorithms that aim to reduce the complexity of models that overfit the training data. In contrast, boosting is an approach to increase the complexity of models that suffer from high bias, that is, models that underfit the training data. | |
C8087 | Distributions of data can have few or many peaks. Distributions with one clear peak are called unimodal, and distributions with two clear peaks are called bimodal. | |
C8088 | N-grams of texts are extensively used in text mining and natural language processing tasks. They are basically a set of co-occuring words within a given window and when computing the n-grams you typically move one word forward (although you can move X words forward in more advanced scenarios). | |
C8089 | Rudolf E. Kálmán | |
C8090 | In General, A Discriminative model models the decision boundary between the classes. A Generative Model explicitly models the actual distribution of each class. A Discriminative model learns the conditional probability distribution p(y|x). Both of these models were generally used in supervised learning problems. | |
C8091 | Matt came to know an old man who went by the name of Stick. Basically, Daredevil was able to use all his senses (except sight) to actually 'see'. He can actually put together an environment in his head by adding together all the elements that his senses pick up. The picture created in his head is 'a world on fire. | |
C8092 | Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. | |
C8093 | Adaptive learning rate methods are an optimization of gradient descent methods with the goal of minimizing the objective function of a network by using the gradient of the function and the parameters of the network. | |
C8094 | Inductive logic programming is the subfield of machine learning that uses first-order logic to represent hypotheses and data. Because first-order logic is expressive and declarative, inductive logic programming specifically targets problems involving structured data and background knowledge. | |
C8095 | A continuous variable can take on any score or value within a measurement scale. In addition, the difference between each of the values has a real meaning. Familiar types of continuous variables are income, temperature, height, weight, and distance. There are two main types of continuous variables: interval and ratio. | |
C8096 | Cross correlation and autocorrelation are very similar, but they involve different types of correlation: Cross correlation happens when two different sequences are correlated. Autocorrelation is the correlation between two of the same sequences. In other words, you correlate a signal with itself. | |
C8097 | Generally speaking, non-probability sampling can be a more cost-effective and faster approach than probability sampling, but this depends on a number of variables including the target population being studied. Certain types of non-probability sampling can also introduce bias into the sample and results. | |
C8098 | Income, although you may consider it to be technically discrete, would likely be treated as a continuous variable. Other discrete variables (such as the number of ER visits per year for a sample of hospitals) may also be treated as continuous even though they are technically discrete. | |
C8099 | In machine learning, classification refers to a predictive modeling problem where a class label is predicted for a given example of input data. Examples of classification problems include: Given an example, classify if it is spam or not. Given a handwritten character, classify it as one of the known characters. |
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